Diabetic Retinopathy (DR) has emerged as a major cause of preventable blindness in recent times. With timely screening and intervention, the condition can be prevented from causing irreversible damage. The work introduces a state-of-the-art Ordinal Regression-based DR Detection framework that uses the APTOS-2019 fundus image dataset. A widely accepted combination of preprocessing methods: Green Channel (GC) Extraction, Noise Masking, and CLAHE, was used to isolate the most relevant features for DR classification. Model performance was evaluated using the Quadratic Weighted Kappa, with a focus on agreement between results and clinical grading. Our Ordinal Regression approach attained a QWK score of 0.8992, setting a new benchmark on the APTOS dataset.
@article{arxiv.2511.14398,
title = {Stage Aware Diagnosis of Diabetic Retinopathy via Ordinal Regression},
author = {Saksham Kumar and D Sridhar Aditya and T Likhil Kumar and Thulasi Bikku and Srinivasarao Thota and Chandan Kumar},
journal= {arXiv preprint arXiv:2511.14398},
year = {2025}
}